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Marvell is the more explicitly connectivity-led choice, while Broadcom reports the larger disclosed AI-semiconductor business. Marvell’s FY2026 data-center disclosures emphasize optical interconnects, custom silicon, switching and storage, with optical interconnect described as roughly half of data-center revenue and custom silicon as about one-quarter. Broadcom reports AI-semiconductor revenue driven by custom accelerators and networking, and its custom-silicon platform spans AI/HPC, networking and storage.
Those statements do not produce a clean winner. Marvell’s headline figure is Q4 FY2026 data-center end-market revenue; Broadcom’s is Q3 FY2026 AI-semiconductor revenue. The periods and category definitions differ, so the amounts must not be ranked directly. The practical choice depends on whether your priority is optical connectivity, a custom ASIC program, Ethernet and scale-out networking, or alignment with a particular ecosystem.
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How to read this comparison
Marvell and Broadcom participate in overlapping parts of the AI data-center stack, but they disclose the business differently. Marvell presents a data-center end-market view that combines optical interconnect, custom silicon, switching and storage. Broadcom highlights an AI-semiconductor category that includes custom AI accelerators and networking, while its annual filing describes custom-silicon technology and intellectual property for customer ASICs in AI/HPC, networking and storage.
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The result is a comparison of strategic exposure and disclosed capabilities, not a market-share ranking. Neither company’s announcements establish that it is exclusive to a customer, that every announced product has reached volume shipment, or that one vendor delivers superior performance in every deployment. Use the figures below as company-reported evidence and keep the fiscal period and category attached to each number.
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At-a-glance comparison
| Decision area | Marvell | Broadcom | What the disclosure does—and does not—show |
|---|---|---|---|
| Reported AI/data-center metric | $1.651 billion of data-center revenue in Q4 FY2026, up 21% year over year (Marvell FY2026 Q4 results presentation, March 5, 2026). | $16.7 billion of AI-semiconductor revenue in Q3 FY2026, up 221% year over year (Broadcom Q3 FY2026 results, September 2, 2026). | Different quarters and category definitions; not a head-to-head revenue comparison. |
| Disclosed business mix | Marvell’s FY2026 proxy describes optical interconnect as roughly half of data-center revenue and custom silicon as approximately one-quarter. | AI-semiconductor growth attributed to custom accelerators and networking; custom-silicon solutions also cover AI/HPC, networking and storage ASICs. | Marvell gives approximate mix descriptions; Broadcom gives a broader AI-semiconductor category. |
| Optical evidence | Expanded 1.6T optical DSP portfolio announced in March 2026, including Ara, Alaska and Nova DSP platforms, Ethernet PHY, silicon photonics, and LPO TIA and laser-driver chipsets. | AI networking is part of the reported growth explanation, but the cited disclosures do not provide a comparable optical-product portfolio list. | A portfolio announcement is not proof of shipment volume or adoption for every component. |
| Custom-silicon evidence | Custom silicon is a named component of Marvell’s data-center growth and is described as about one-quarter of data-center revenue in the proxy. | Custom accelerators are a stated driver of AI-semiconductor growth; the FY2025 Form 10-K describes technology and IP for customer ASICs. | Neither disclosure provides comparable program counts, margins or independent customer evaluations. |
| Named ecosystem relationships | Collaboration with NVIDIA connecting Marvell custom silicon and networking capabilities with NVIDIA’s AI ecosystem through NVLink Fusion. | AI-accelerator and Ethernet-networking collaboration with OpenAI; extended partnership supporting Meta’s MTIA custom silicon. | Announcements establish stated scope, not exclusivity, realized revenue or technical superiority. |
What Marvell discloses about AI data-center networking
Data-center growth spans connectivity and silicon
Marvell reported $1.651 billion in data-center revenue for Q4 FY2026, a 21% year-over-year increase. The company attributed sequential growth to optical interconnects, custom silicon, switching and storage. This is an end-market figure: it is useful for understanding the scale Marvell assigns to data-center activity, but it is not a standalone optical, switching or custom-chip revenue line.
Marvell’s FY2026 proxy adds an approximate mix description: optical interconnect represented roughly half of data-center revenue, while custom silicon represented approximately one-quarter. These are company descriptions rather than audited standalone segment results. They indicate that connectivity is central to Marvell’s data-center positioning and that custom silicon is a substantial companion business, not a peripheral experiment.
The 1.6T optical roadmap
In a March 2026 announcement, Marvell expanded its 1.6T optical digital-signal-processor portfolio. The named platforms are Ara, Alaska and Nova. The announcement also covers Ethernet PHY, silicon-photonics technology, and linear pluggable optics (LPO) transimpedance-amplifier and laser-driver chipsets.
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For an AI cluster, that mix matters because optical components sit at the interfaces that move traffic between compute systems, switches and longer-reach links. DSPs process high-speed optical signals; PHY devices provide electrical interfaces; silicon photonics and laser-driver or TIA components address the optical conversion chain. The announcement shows breadth across that chain, but it does not state shipment volumes, production timing for every device, power figures, latency, attach rates or customer adoption.
What Marvell’s positioning means for a buyer
Marvell is the more directly documented option when your evaluation starts with optical and electrical connectivity. Its public description gives you a roadmap to investigate at several layers rather than a single accelerator part. That can simplify a platform discussion when the bottleneck is moving data through a large fabric, but a roadmap alone cannot establish that a specific device is qualified for your design or available in the quantity and schedule you need.
For a custom-chip program, Marvell’s disclosed data-center mix supports treating the company as more than an interconnect supplier. You still need program-specific answers: which process node and package are available, what interfaces are supported, how verification and firmware are handled, what intellectual property can be reused, and when engineering samples and production capacity are committed. None of those details is quantified in the cited disclosures.
What Broadcom discloses about AI accelerators and networking
A much larger, differently defined AI-semiconductor figure
Broadcom reported $16.7 billion in AI-semiconductor revenue for Q3 FY2026, up 221% year over year. The company said the increase was driven by demand for custom AI accelerators and networking. Broadcom also gave a Q4 AI-semiconductor revenue expectation of $21.7 billion. That $21.7 billion is guidance, not a completed quarter’s result, and should not be mixed with the Q3 actual.
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Custom silicon as a platform business
Broadcom’s FY2025 Form 10-K says its custom-silicon solutions provide technology and intellectual property for customer ASICs in AI/HPC, networking and storage. That wording describes an enabling platform for customers’ chips, rather than a retail accelerator sold under a single Broadcom model name. A serious evaluation should therefore focus on the complete engagement: architecture support, reusable IP, physical implementation, packaging, software interfaces, validation and the customer’s manufacturing plan.
Broadcom’s Q3 explanation links custom accelerators and networking in the same AI-semiconductor growth narrative. This is relevant to hyperscale designs that combine an application-specific accelerator with a high-bandwidth fabric. It does not, by itself, identify the exact switch, SerDes, optical or software components in a particular program, nor does it quantify the portion of revenue from each.
Networking evidence in the public record
Broadcom’s cited disclosures establish networking as a material part of its AI-semiconductor growth and of its custom-silicon scope. They do not provide, in the supplied material, a Marvell-style list of 1.6T optical DSP platforms or a like-for-like table of optical components. If optical reach, LPO interoperability or a particular PHY is a gating requirement, request the device-level documentation rather than inferring it from the aggregate AI figure.
How the announced ecosystem relationships differ
Marvell and NVIDIA: NVLink Fusion
On March 31, 2026, Marvell and NVIDIA announced a collaboration connecting Marvell custom-silicon and networking capabilities with NVIDIA’s AI ecosystem through NVLink Fusion. The announcement is evidence that Marvell is positioning its custom designs and connectivity around that ecosystem. It does not establish exclusivity, a guaranteed design win, production revenue or superiority over other implementation paths.
For a team already planning around NVIDIA technologies, the practical question is compatibility and scope. Ask which interfaces, validation resources, software hooks and support obligations are included in the specific program, and whether the collaboration covers the exact accelerator, scale-up link or scale-out network you intend to deploy. The public announcement alone does not answer those implementation questions.
Broadcom, OpenAI and Ethernet networking
Broadcom announced an AI-accelerator and Ethernet-networking collaboration with OpenAI on October 13, 2025. This identifies a customer relationship spanning compute and networking, but the announcement does not state exclusivity, volume, product specifications or realized revenue. Treat it as evidence of ecosystem activity and customer engagement, not as a transferable performance result for every Broadcom-based design.
Broadcom and Meta’s MTIA program
On April 14, 2026, Broadcom announced an extended partnership supporting Meta’s MTIA custom silicon. That relationship is relevant if your organization studies hyperscaler-developed accelerators or wants a supplier experienced with a customer ASIC program. It still does not disclose the complete chip architecture, manufacturing allocation, deployment scale or financial contribution, so it should not be used as a proxy for your own project’s outcome.
Networking architecture: where each company may fit
Scale-up connectivity
Scale-up links connect processors and accelerators inside a tightly coupled training or inference domain. The critical questions are the supported protocol, signaling rate, topology, switch or retimer placement, latency, error handling, power and software behavior. Marvell’s NVLink Fusion collaboration is the clearest disclosed ecosystem signal for this category, but the supplied announcement does not list project-level specifications. Broadcom’s custom-accelerator work may also participate in scale-up designs, yet the public figures cited here do not identify the relevant implementation.
Scale-out Ethernet fabrics
Scale-out fabrics connect many servers or accelerator trays across rows and halls. They depend on Ethernet switching, high-speed PHYs and optical links, congestion control, telemetry and operational tooling. Marvell’s disclosed mix explicitly includes switching and an extensive optical component roadmap. Broadcom’s reported AI growth explicitly includes networking, and its OpenAI announcement names Ethernet networking. To choose between them, compare the actual switch, PHY, DSP and optics combinations proposed for your topology rather than comparing corporate revenue totals.
Optical reach and power trade-offs
At higher link speeds, the optical module, host electrical interface and signal-processing method affect reach, power, thermal design and serviceability. Marvell’s announcement names both conventional DSP platforms and LPO-related TIA and laser-driver chipsets, giving buyers several architectures to investigate. The announcement does not state power budgets, reach tables or interoperability results, so those must come from the component data sheets and your own qualification process.
What the financial disclosures can—and cannot—tell you
Do not rank $1.651 billion against $16.7 billion
Marvell’s $1.651 billion is Q4 FY2026 data-center end-market revenue. Broadcom’s $16.7 billion is Q3 FY2026 AI-semiconductor revenue. The quarters differ, and the categories differ. A direct ratio would create false precision because it would compare an end-market grouping with a product-and-application grouping.
Separate actual results from guidance
Broadcom’s $16.7 billion is reported Q3 revenue; its $21.7 billion Q4 expectation is management guidance. Guidance can change as demand, supply, customer schedules and accounting classifications change. Keep it in a forecast column in your model and label the period and status explicitly.
Approximate mix is not a segment P&L
Marvell’s proxy description that optical interconnect is roughly half of data-center revenue and custom silicon approximately one-quarter helps explain strategic emphasis. It does not provide audited standalone revenue, gross margin, operating expense or cash-flow figures for those categories. The cited material also does not provide comparable margins, market share or customer-satisfaction data for either company.
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Choose the evidence that matches your primary constraint
| If your primary requirement is… | Which public evidence is more directly relevant | What you still need to verify |
|---|---|---|
| A broad optical-connectivity roadmap | Marvell’s 1.6T Ara, Alaska and Nova DSP announcement, plus Ethernet PHY, silicon-photonics, LPO TIA and laser-driver components. | Production status, data-sheet specifications, interoperability, power, reach, lead time and support. |
| A custom ASIC engagement across AI/HPC and networking | Broadcom’s Form 10-K description of custom-silicon technology and IP for customer ASICs. | Available IP blocks, design-service scope, process and package options, verification plan, NRE, schedule and capacity. |
| A data-center portfolio visibly combining optics, switching and custom silicon | Marvell’s Q4 FY2026 data-center disclosure and proxy mix description. | Product-level qualification, software integration, total system cost and deployment references for your topology. |
| AI-semiconductor scale tied to custom accelerators and networking | Broadcom’s $16.7 billion Q3 FY2026 AI-semiconductor result and 221% year-over-year growth. | How the reported category maps to your product, customer allocation, delivery schedule and future capacity. |
| Alignment with a named ecosystem | NVIDIA–Marvell NVLink Fusion; Broadcom collaborations with OpenAI and Meta’s MTIA. | Whether your exact design is covered, what interfaces and software are supported, and whether supply is committed. |
Use a weighted scorecard instead of a brand verdict
Assign weights that reflect your deployment: optical and electrical bandwidth, protocol support, latency, power, package and thermal constraints, software and telemetry, custom-ASIC services, manufacturing schedule, qualification evidence and commercial terms. Score only documented, project-relevant evidence. Keep an explicit “not stated” value for any category where neither company has supplied a comparable number; do not convert an announcement into a score.
Match the supplier to the stage of your program
- Architecture phase: compare interface roadmaps, topology options and ecosystem compatibility. Marvell’s optical portfolio announcement may be useful for defining link options; Broadcom’s custom-silicon disclosures may help frame an ASIC engagement.
- Design phase: obtain preliminary data sheets, reference designs, IP lists, package constraints and verification ownership. Confirm which announced components are orderable and which remain roadmap items.
- Qualification phase: test signal integrity, thermal behavior, optical interoperability, fault recovery, congestion behavior and software observability under your traffic patterns. Public revenue growth is not a substitute for these tests.
- Deployment phase: validate supply commitments, firmware support, replacement policy, field diagnostics and escalation paths. A technically suitable part can still fail a rollout if the delivery and support model is unclear.
Questions to put to both vendors
- Which exact devices, IP blocks and package options are available for the proposed accelerator or network design today?
- For every roadmap item, what is the sampling date, production date, qualification status and committed capacity?
- What are the measured bandwidth, latency, power and thermal limits for the proposed configuration, and under what test conditions?
- Which optical modules, cables, switches, PHYs, DSPs and host interfaces have been interoperated and validated together?
- What software, firmware, telemetry and congestion-management support is included, and who owns integration defects?
- For a custom ASIC, which parts of architecture, RTL, physical design, verification, packaging and bring-up are supplied by the vendor?
- What non-recurring engineering charges, minimum volumes, change-control rules and end-of-life commitments apply?
- Does the named ecosystem collaboration cover your intended use case, or is additional certification or licensing required?
Common interpretation errors
Calling one company the overall winner
There is no single winner independent of the criterion. Marvell has the clearer public optical-connectivity emphasis; Broadcom has the larger disclosed AI-semiconductor figure and broad custom-accelerator and networking narrative. Those are different strengths, measured in different ways.
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Treating a roadmap as shipped share
The 1.6T portfolio announcement demonstrates product direction and component breadth. It does not establish shipment volume, market share or customer adoption for Ara, Alaska, Nova or every named chipset. Use production and qualification evidence when making a sourcing decision.
Assuming a partnership is exclusive
The NVIDIA, OpenAI and Meta announcements describe collaborations and partnerships. None of the cited material says that the relationship is exclusive, guarantees revenue or prevents the customer from using other suppliers. Confirm contractual scope for the specific program.
Using company language as independent validation
Terms such as leading, strategic or expanded describe a company’s announcement. They are not independent benchmarks. Request test conditions, interoperability reports, availability dates and customer references that apply to your workload.
Bottom line for different buyer profiles
For an optics-first networking program
Start with Marvell’s documented optical-interconnect emphasis and 1.6T component roadmap, then qualify the exact DSP, PHY, silicon-photonics or LPO configuration. The public evidence is strongest on portfolio breadth, not on shipment scale or measured system performance.
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Broadcom’s reported AI-semiconductor growth and its stated custom-ASIC technology and IP across AI/HPC, networking and storage make it a strong candidate for an architecture-to-production discussion. The relevant proof will be program-specific execution, capacity and qualification, not the aggregate revenue number alone.
For an NVIDIA-aligned custom design
Marvell’s NVLink Fusion collaboration is the directly disclosed relationship to investigate. Confirm the interfaces, software, validation and commercial boundaries for your design before treating the announcement as a selection advantage.
For an Ethernet-centered AI fabric
Both companies have relevant evidence: Marvell through its stated optical and switching mix, and Broadcom through AI-semiconductor growth attributed partly to networking and its announced Ethernet collaboration with OpenAI. Compare the complete fabric bill of materials and operating model rather than selecting on corporate scale.
Final assessment
Marvell and Broadcom are best understood as overlapping but differently disclosed suppliers. Marvell’s public case is built around data-center connectivity, a substantial optical-interconnect presence and custom silicon. Broadcom’s public case is built around rapidly growing AI-semiconductor revenue, custom accelerators, networking and a customer-ASIC platform that spans AI/HPC, networking and storage.
The defensible conclusion is therefore conditional: Marvell is the more explicitly documented fit when optical connectivity breadth is the starting point; Broadcom is the more visibly scaled option in the reported AI-semiconductor category and merits close evaluation for large custom-accelerator programs. Because the metrics, periods and product boundaries differ, make the final choice with device-level specifications, qualification results, delivery commitments and contract terms—not with a single headline revenue comparison.
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